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GPU-Accelerated Quantification Filters for Analytical Queries in Multidimensional Databases

  • In online analytical processing (OLAP), filtering elements of a given dimensional attribute according to the value of a measure attribute is an essential operation, for example in top-k evaluation. Such filters can involve extremely large amounts of data to be processed, in particular when the filter condition includes “quantification” such as ANY or ALL, where large slices of an OLAP cube have toIn online analytical processing (OLAP), filtering elements of a given dimensional attribute according to the value of a measure attribute is an essential operation, for example in top-k evaluation. Such filters can involve extremely large amounts of data to be processed, in particular when the filter condition includes “quantification” such as ANY or ALL, where large slices of an OLAP cube have to be computed and inspected. Due to the sparsity of OLAP cubes, the slices serving as input to the filter are usually sparse as well, presenting a challenge for GPU approaches which need to work with a limited amount of memory for holding intermediate results. Our CUDA solution involves a hashing scheme specifically designed for frequent and parallel updates, including several optimizations exploiting architectural features of Nvidia’s Fermi and Kepler GPUs.show moreshow less

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Metadaten
Document Type:Conference Proceeding
Conference Type:Konferenzartikel
Zitierlink: https://opus.hs-offenburg.de/1598
Bibliografische Angaben
Title (English):GPU-Accelerated Quantification Filters for Analytical Queries in Multidimensional Databases
Conference:East European Conference on Advances in Databases and Information Systems and Associated Satellite Events (18. : September 7-10, 2014 : Ohrid, Macedonia)
Author:Peter Tim Strohm, Steffen Wittwer, Alexander Haberstroh, Tobias LauerStaff MemberGND
Edition:1.
Year of Publication:2015
Place of publication:Cham
Publisher:Springer
First Page:229
Last Page:242
Parent Title (English):New Trends in Database and Information Systems II
Editor:Nick Bassiliades, Mirjana Ivanović, Margita Kon-Popovska, Yannis Manolopoulos, Themis Palpanas, Goce Trajcevski, Athena Vakali
Volume:2
ISBN:978-3-319-10518-5
ISBN:978-3-319-10517-8
Language:English
Inhaltliche Informationen
Institutes:Fakultät Elektrotechnik und Informationstechnik (E+I) (bis 03/2019)
Institutes:Bibliografie
GND Keyword:Datenbank; Informationssystem
Tag:Analytical Query; Filter Dimension; Hash Function; Hash Table; Target Path
Formale Angaben
Open Access: Closed Access 
Licence (German):License LogoUrheberrechtlich geschützt
Opac ID:Link zum Online-Katalog
Comment:
Parts of the research described in this paper were presented by the authors at Nvidia’s GPU Technology Conference in San Jose, CA (USA) in March 2014.